Evidence map›Paper›PMID 37266885›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

TAToo: vision-based joint tracking of anatomy and tool for skull-base surgery.

Zhaoshuo Li, Hongchao Shu, Ruixing Liang, Anna Goodridge, Manish Sahu, Francis X Creighton, Russell H Taylor, Mathias Unberath

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Perception-first digital twin for augmented reality microsurgery.International journal of computer assisted radiology and surgery · 2026
    Article
  3. Markerless inside-out tool tracking for endoscopic spine surgery: a benchmarking study and clinical dataset.International journal of computer assisted radiology and surgery · 2026
    Article
  4. Integration of Trackerless Surface Reconstruction-Based Surgical Navigation With Exoscopic Trans-Mastoid Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
    Article
  5. Review
  6. An endoscopic chisel: intraoperative imaging carves 3D anatomical models.International journal of computer assisted radiology and surgery · 2024
    Article
  7. Review
  8. Fully immersive virtual reality for skull-base surgery: surgical training and beyond.International journal of computer assisted radiology and surgery · 2024
    Article
  9. Twin-S: a digital twin for skull base surgery.International journal of computer assisted radiology and surgery · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhaoshuo LiJohns Hopkins University, Baltimore, MD, USA. zli122@jhu.edu.ORCID http://orcid.org/0000-0001-5874-4713
Hongchao ShuJohns Hopkins University, Baltimore, MD, USA.
Ruixing LiangJohns Hopkins University, Baltimore, MD, USA.
Anna GoodridgeJohns Hopkins University, Baltimore, MD, USA.
Manish SahuJohns Hopkins University, Baltimore, MD, USA.
Francis X CreightonJohns Hopkins Medicine, Baltimore, MD, USA.
Russell H TaylorJohns Hopkins University, Baltimore, MD, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, USA.

Funding

Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base SurgeryK08DC019708 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI CREIGHTON, FRANCIS XAVIER · 2021 to 2025
$959k
NIDCD NIH HHS K08 DC019708NIDCD NIH HHS K08 Grant DC019708
6 · The paper itself

Abstract

purposeTracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation.

methodsWe present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of the patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level.

resultsWe validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below [Formula: see text]. We further illustrate how TAToo may be used in a surgical navigation setting.

conclusionsWe present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.

Indexed as

Neurosurgical ProceduresSurgery, Computer-AssistedComputer SimulationHumansSkull Base3D motion estimationComputer-assisted interventionsComputer visionDeep learningImage-based navigation

Identifiers

PMID37266885
PMCPMC11332659

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.